The enthusiasm regarding the factor analytic approach to uncover underlying theoretical constructs was not universally shared. In particular, the interpretation of a latent variable as a hypothetical construct was much criticized. One of the earliest criticism came from Spearman's colleague, Karl Pearson, widely regarded as the founder of modern mathematical statistics. The two men constantly feuded with each other throughout their careers due to their opposing scientific philosophies [1]}. Whereas Spearman strove to discover the fundamental laws of psychology and invent a method by which he can objectively measure latent traits such as intelligence, Pearson did not see a place for unobserved and subsequently unmeasurable phenomena in the scientific enterprise. Incidentally, [2]} had pioneered a method closely related to, and often conflated with Factor Analysis (FA) - principle component analysis (PCA). Pearson's PCA, which was later formulated as multivariate technique by [3]}, was introduced not as an explanatory model, but as a dimensionality reduction technique in which the data is geometrically transformed (i.e. projected) into a lower dimensional subspace.
